Image-based inventory control system using advanced image recognition

ABSTRACT

Systems for monitoring an inventory condition of objects based on captured images are described. An exemplary system includes at least one storage drawer, each storage drawer including a plurality of storage locations for storing objects; and a data storage device storing reference data for each storage drawer. The reference data includes, for each region of interest corresponding to each respective storage drawer, at least one of a reference image signature representing attributes of the region of interest when a stored object is present, and a reference image signature representing attributes of the region of interest when the stored object is not present. A data processor of the system is configured to derive an image signature representing attributes of a region of interest corresponding to a drawer using a captured image of the drawer; and determine an inventory condition of the drawer according to the stored reference data corresponding to the region of interest and the derived image signature representing attributes of the captured image of the region of interest.

RELATED APPLICATIONS

This application claims the benefit of priority from provisional patentapplication No. 61/087,565, entitled IMAGE-BASED INVENTORY CONTROLSYSTEM, the disclosure of which is incorporated herein in its entirety.

FIELD OF DISCLOSURE

The present disclosure relates to an inventory control system, morespecifically, to an image-based inventory control system for determiningan inventory condition of objects stored in the system, based oncaptured images of various storage locations of the system.

BACKGROUND AND SUMMARY OF THE DISCLOSURE

When tools are used in a manufacturing or service environment, it isimportant that tools be returned to a storage unit, such as a tool box,after use. Employers typically perform a manual inventory check of thetool box to minimize or eliminate the problem of misplacement or theftof expensive tools. Companies can conduct random audits of employee'stoolbox to prevent theft and monitor tool location.

Some industries have high standards for inventory control of tools, forpreventing incidents of leaving tools in the workplace environment wherethey could cause severe damages. For the aerospace industry, it isimportant to ensure that no tools are accidentally left behind in anaircraft or missile being manufactured, assembled or repaired. TheAerospace Industries Association even establishes a standard calledNational Aerospace Standard including recommended procedures, personnelmanagement and operations to reduce foreign object damage (FOD) toaerospace products. FOD is defined as any object not structurally partof the aircraft. The most common foreign objects found are nuts, bolts,safety wire, and hand tools. Inventory control over tools is critical toprevent tools from being left in an aircraft.

Some toolboxes includes build-in inventory determination features totrack inventory conditions of tools stored in those toolboxes. Forexample, some toolboxes dispose contact sensors, magnetic sensors orinfrared sensors in or next to each tool storage locations, to detectwhether a tool is placed in each tool storage location. Based on signalsgenerated by the sensors, the toolboxes are able to determine whetherany tools are missing. While this type of inventory check may be usefulto some extents, it suffers from various drawbacks. For instance, if asensor detects that something is occupying a storage location, thetoolbox will determine that no tool is missing from that storagelocation. However, the toolbox does not know whether the right kind oftool is indeed placed back in the toolbox or it is just some objectsplaced in the storage location to cheat the system. Furthermore,disposing sensors for numerous storage locations in a toolbox is tediousand costly, and the large number of sensors is prone to damages ormalfunctions which will produce false negative or positive alarms.

Accordingly, there is a need for an effective inventory control systemthat that could assist tracking and accounting for usage of tools andwhether they are properly put back after usage. There is also a need foran inventory control system which knows exactly what tool is removed orreturned to a tool box. Furthermore, as multiple workers may have accessto the same tool box, there is another need for an inventory controlsystem that can track a user and his or her usage of tools, to determineresponsibilities for any tool loss or misplacement.

This disclosure describes various embodiments of highly automatedinventory control systems that utilize unique machine imaging andmethodology for identifying an inventory condition in the storage unit.Illustrative features include the ability to process complex image datawith efficient utilization of system resources, autonomous image andcamera calibrations, identification of characteristics of tools fromimage data, adaptive timing for capturing inventory images, efficientgeneration of reference data for checking inventory status, autonomouscompensation of image quality, etc.

An exemplary inventory control system includes at least one storagedrawer, each storage drawer including a plurality of storage locationsfor storing objects; a data storage device storing reference data foreach storage drawer, wherein the reference data includes, for eachregion of interest corresponding to each respective storage drawer, atleast one of a reference image signature representing attributes of theregion of interest when a stored object is present, and a referenceimage signature representing attributes of the region of interest whenthe stored object is not present; and a data processor. The dataprocessor is configured to derive an image signature representingattributes of a region of interest corresponding to a drawer using acaptured image of the drawer; and determine an inventory condition ofthe drawer according to the stored reference data corresponding to theregion of interest and the derived image signature representingattributes of the captured image of the region of interest. The capturedimage of a drawer may be an image of the entire drawer or a partialimage of the drawer.

In one aspect, the image signature is derived based on a pixel numberdistribution of an image relative to preset image attributes. The dataprocessor may generate the image signature of the image by accessingdata including the pixel number distribution relative to preset imageattributes; identifying a group of pixels sharing a set of common imageattributes based on a number of pixels sharing the common imageattributes; identifying one or more groups of pixels sharing a differentset of common image attributes based on a number of pixels sharing thedifferent set of common image attributes; and generating the imagesignature based on the common image attributes of the identified pixelgroups.

Each storage location may be configured to store a pre-designated tool,and each storage location may be associated with one or more pre-definedregions of interest. In one embodiment, the data processor determines aninventory condition of the pre-designated tool of a respective storagelocation based on a correlation of the image signature of each region ofinterest associated with the storage location, and the reference imagesignature corresponding to each region of interest associated with thestorage location.

A method for using in an inventory control system for determining aninventory condition of objects stored in the system, the systemcomprising at least one storage drawer for storing objects, a datastorage device storing reference data for each storage drawer, whereinthe reference data includes, for each region of interest correspondingto each respective storage drawer, at least one of a reference imagesignature representing attributes of the region of interest when astored object is present, and a reference image signature representingattributes of the region of interest when the stored object is notpresent; the method comprising: capturing an image of a drawer; derivingan image signature representing attributes of a region of interestcorresponding to a drawer; and determining an inventory condition of thedrawer according to the stored reference data corresponding to theregion of interest in connection with the drawer and the derived imagesignature representing attributes of the captured image of the region ofinterest corresponding to the storage drawer.

According to another embodiment, an inventory control system accordingto this disclosure includes at least one storage drawer, each storagedrawer including a foam layer forming a plurality of storage locationsfor storing objects, wherein each storage location is configured tostore a pre-designated object and each storage location is formed by acutout of the foam layer corresponding an object stored in the storagelocation; an image sensing device configured to capture an image of oneof the storage drawers; a data storage system storing reference datacorresponding to each storage drawer, wherein the reference dataincludes information of one or more pre-defined regions of interestcorresponding to each storage location in the drawer, and objects storedin each storage location in the drawer; and a data processor. The dataprocessor is configured to access the captured image of the drawer;access the reference data corresponding to the drawer; determine aninventory condition of the drawer based on the stored reference datacorresponding to the drawer and the captured image of the storagedrawer. The reference data is adapted from a data file based on whichcutouts of the foam layer are created. In one aspect, the data processorimports the data file based on which cutouts of the foam layer arecreated, and derives the reference data from (change to based on) thedata file.

According to still another embodiment, a novel method is proposed forpreparing reference data for use in an inventory control system fordetermining an inventory condition of objects stored in the system. Thesystem includes at least one storage drawer, each storage drawerincluding a foam layer forming a plurality of storage locations forstoring objects, wherein each storage location is configured to store apre-designated object and each storage location is formed by a cutout ofthe foam layer corresponding an object stored in the storage location,the system determines the inventory condition of a respective drawerbased on a captured image of the respective drawer and the referencedata. The method comprises: for each respective drawer, importing, froma database, a data file based on which cutouts of the foam layer for therespective drawer are created, wherein the data files includespositional information of each cutout in the respective drawer; andusing a data processing device to create a data structure specifyingpositional information of each cutout in each respective drawer and atleast one region of interest corresponding to each cutout, according tothe imported data file.

Methods and systems described herein may be implemented using one ormore computer systems and/or appropriate software.

It is understood that embodiments, steps and/or features describedherein can be performed, utilized, implemented and/or practiced eitherindividually or in combination with one or more other steps, embodimentsand/or features.

Additional advantages and novel features of the present disclosure willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing, or may be learned by practice of the present disclosure. Theembodiments shown and described provide an illustration of the best modecontemplated for carrying out the present disclosure. The disclosure iscapable of modifications in various obvious respects, all withoutdeparting from the spirit and scope thereof. Accordingly, the drawingsand description are to be regarded as illustrative in nature, and not asrestrictive. The advantages of the present disclosure may be realizedand attained by means of the instrumentalities and combinationsparticularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the accompanying drawings, wherein elements having thesame reference numeral designations represent like elements throughoutand wherein:

FIGS. 1 a and 1 b show exemplary storage units in which embodimentsaccording to this disclosure may be implemented;

FIG. 2 shows details inside an exemplary storage drawer operated in theopen mode;

FIG. 3 shows an exemplary tool storage system according to thisdisclosure;

FIGS. 4 a-4 c and 4 e are different views of the tool storage systemshown in FIG. 3;

FIG. 4 d illustrates how an exemplary image is stitched together;

FIG. 5 shows exemplary tool cutout, buffer zone and dilation zone;

FIGS. 6 a and 6 b are exemplary identifier designs for use in thisdisclosure;

FIGS. 7 a-7 e and 8 a-8 d illustrates examples of image calibration;

FIG. 9 is a block diagram of an exemplary networked inventory controlsystem; and

FIG. 10 a-10 d are illustrative images of an exemplary audit record andimages taken during access to an exemplary system according to thisdisclosure.

DETAILED DESCRIPTIONS OF ILLUSTRATIVE EMBODIMENTS

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. Specifically, operations ofillustrative embodiments that utilize machine vision to identifyinventory conditions of a storage unit are described in the context oftool management and tool inventory control. It will be apparent,however, to one skilled in the art that concepts of the disclosure maybe practiced or implemented without these specific details. Similarconcepts may be utilized in other types of inventory control systemssuch as warehouse management, jewelry inventory management, sensitive orcontrolled substance management, mini bar inventory management, drugmanagement, vault or security box management, etc. In other instances,well-known structures and devices are shown in block diagram form inorder to avoid unnecessarily obscuring the present disclosure. As usedthroughout this disclosure, the terms “a captured image”, “capturedimages”, “an image”, or “images” of an object, an area or a drawer aredefined as an image of the entire object, area or drawer, or a partialimage of the object, area or drawer.

Overview of Exemplary Inventory Control Systems

FIGS. 1 a and 1 b show exemplary storage units in which inventorycontrol systems according to this disclosure may be implemented. FIG. 1a is an exemplary tool storage system 100 including multiple storagedrawers 120. Each storage drawer 120 includes multiple storage locationsfor storing various types of tools. As used throughout this disclosure,a storage location is a location in a storage system for storing orsecuring objects. In one embodiment, each tool has a specificpre-designated storage location in the tool storage system.

Each storage drawer operates between a close mode, which allows noaccess to the contents of the drawer, and an open mode, which allowspartial or complete access to the contents of the drawer. When a storagedrawer moves from a close mode to an open mode, the storage drawerallows increasing access to its contents. On the other hand, if astorage moves from an open mode to a close mode, the storage drawerallows decreasing access to its contents. As shown in FIG. 1 a, allstorage drawers 120 are in close mode.

A locking device may be used to control access to the contents of thedrawers 120. Each individual drawer 120 may have its own lock ormultiple storage drawers 120 may share a common locking device. Onlyauthenticated or authorized users are able to access to the contents ofthe drawers.

The storage drawers may have different sizes, shapes, layouts andarrangements. FIG. 1 b shows another type of tool storage system 200which includes multiple storage shelves or compartments 220 and a singledoor 250 securing access to the storage shelves 250. The storage shelvesor compartments may come in different sizes, shapes, layouts andarrangements.

FIG. 2 shows details inside an exemplary storage drawer 120 in the openmode. Each storage drawer 120 includes a foam base 180 having aplurality of storage locations, such as tool cutouts 181, for storingtools. Each cutout is specifically contoured and shaped for fittinglyreceiving a tool with corresponding shapes. Tools may be secured in eachstorage location by using hooks, Velcro, latches, pressures from thefoam, etc.

An exemplary inventory control system according to this disclosuredetermines an inventory condition of objects by capturing and processingimages of storage locations that are used to store the objects. Thesystem includes various sub-systems needed for performing differentfunctions, including: a storage subsystem, an imaging subsystem, anaccess control subsystem, a power supply subsystem, a user interfacesubsystem, a data processing subsystem, a sensor subsystem, and anetwork subsystem. It is understood that the list of subsystems is forillustration purpose only and is not exhaustive. An exemplary systemaccording to this disclosure may be implemented using more or lesssubsystems depending on design preference. Furthermore, not all thesubsystems have to be integral part of the inventory control system. Inone embodiment, various functions described in this disclosure may beimplemented on a distributed architecture including multiple subsystemsconnected to a data transmission network. Each sub-system connected bythe data transmission network contributes a portion of the functionalityfor the entire system. In this way, the total system functionality isdistributed across multiple sub-systems that communicate via the datatransmission network. A communications methodology or combination ofmethodologies can be established to allow seamless information sharingacross the network to the various sub-systems or data processing systemsexternal to the inventory control system. The methodology may includeTransmission Control Protocol/Internet Protocol (TCP/IP), DISCO, SOAP,XML, DCOM, CORBA, HTML, ASP, Microsoft .NET protocols, Microsoft .NETWeb Services and other alternate communications methodologies.

(1) Storage Subsystem

The storage subsystem includes multiple storage locations for storingobjects, such as those illustrated in FIGS. 1 a, 1 b and 2. It isunderstood that other types of storage systems that are used for storingobjects may be used to implement the storage subsystem.

(2) Imaging Subsystem

The image subsystem includes one or more image sensing devicesconfigured to capture images of part or all contents or storagelocations, and illumination devices providing needed illuminations. Theimage sensing device may be lens-based cameras, CCD cameras, CMOScameras, video cameras, or any types of device that captures images. Theimage sensing devices may optionally include a preprocessing means forpreprocessing captured images. Examples of the preprocessing includedata compression, enhancement, information retrieval, data mining,deriving features of item of interest in certain pre-defined areas orregions of interest and the surrounding areas, etc. The image sensingdevices convey raw image data or any preprocessed data for processing orviewing by a data processing system located locally or remote to theimage subsystem. The camera may include a network interface forconnecting to a data transmission network for transmitting dataincluding the captured images, preprocessed images, information derivedfrom the captured images, etc.

(3) Access Control Subsystem

The access control sub-system includes one or more identity verificationdevices for verifying or authenticating identity and an authorizationlevel of a user intending to access objects stored in the storagesubsystem; and one or more locking devices controlling access to thestorage locations in the storage subsystem. For example, the accesscontrol subsystem keeps some or all storage drawers locked in a closedposition until the identity verification device authenticates a user'sauthorization for accessing the storage subsystem.

The access control subsystem may use one or more access authenticationmeans to verify a user's authorization levels, such as by using a keypad to enter an access code or passwords, a keycard reader to read froma key card or fobs authorization level of a user holding the card orfob, biometric methods such as fingerprint readers or retinal scans,facial recognitions, magnetic or radio frequency ID cards, proximitysensor, occupancy sensor, and/or manual entry or other methods. In oneembodiment, access to each storage location or storage drawer iscontrolled and granted independently. Based on an assigned authorizationor access level, a user may be granted access to one or more drawers,but not to others.

(4) Power Supply Subsystem

The power supply subsystem provides and manages power supplied toinventory access control system. The power supply subsystem includes oneor more power sources including connections to one or more AC powersources and/or one or more power storage devices, such as batteries,and/or battery chargers. The subsystem may also include needed circuitsand controllers for tracking a power supply condition of the system,such as switching between AC power supply and battery power supply,monitoring a charge state of the battery or batteries, etc. An externalcharger may be provided so that batteries may be charged apart from thesystem and kept in reserve for quick swap out of spent batteries on thesystem. In one embodiment, a remote battery charger may be connected toa data network coupled to the inventory control system to report acharge condition of a battery being charged at a remote charger. Inanother embodiment, the power supply subsystem is coupled to a mastercomputer remote to the inventory control system, providing informationregarding a power supply condition of the inventory control system tothe master computer, such as power failures, charging conditions,available power supplies, etc.

(5) User Interface Subsystem

The user interface subsystem includes devices for communicating with auser of the inventory control system, such as one or more of a display,a microphone, a speaker, a keyboard, a mouse, or any other devices thatoutput information to the user and/or allow the user to inputinformation. The user may be a technician who intends to access objectsor tools in the inventory control system, or a manager that manages theinventory condition of the system.

(6) Data Processing Subsystem

The data processing subsystem, such as a computer, is in charge ofprocessing images captured by image sensing devices of the imagingsubsystem and/or generate reports of inventory conditions. Imagescaptured or formed by the image sensing devices are processed by thedata processing system for determining an inventory condition. The terminventory condition as used throughout this disclosure means informationrelating to an existence or non-existence condition of objects.

The data processing subsystem may be implemented as an integral part ofthe inventory control system; or with a remote computer having a datalink, such as a wired or wireless link, coupled to the inventory controlsystem; or a combination of a computer integrated in the inventorycontrol system and a computer remote to the inventory control system.Detailed operations for forming images and determining inventoryconditions will be discussed shortly.

In one embodiment, the data processing subsystem holds user and/orobject information and history. A user may query the data processingsubsystem for status, setting up users, handling exceptions, systemmaintenance, and so on. In another embodiment, a host system, externalto the inventory control system, is provided and coupled to the computerintegral to the inventory control system via a data link or network, forproviding supervisory or maintenance functions, viewing audit images,providing reports and summaries of item status and location, etc.

(7) Sensor Subsystem

Depending on needed functions, a sensor sub-system may be provided, inaddition to the imaging subsystem, to sense attributes of objects orareas of interest or their surroundings. These attributes may be used inconjunction with image information obtained by the image sensing devicesof the imaging subsystem to enhance the recognition of objects or statesthereof. The sensor subsystem may include sensors for sensing conditionssuch as pressure, light, force, strain, magnetic field, capacitivesense, RFID, electric field, motion, acceleration, orientation,position, location, GPS, Radio Frequency triangulation, lighttriangulation, proximity, gravitational field strength and/or direction,touch and simple manual entry or interaction may be included as input.

(8) Network Subsystem

The network subsystem allows the subsystems to form data communicationsbetween the subsystems and/or to interface with a data communicationnetwork and/or another data processing system external to the inventorycontrol system, for performing data communications. Examples of devicesfor implementing the network interface include one or more of anEthernet interface, RS-232, RS-422, RS-485, Access bus, I2C, IE Bus, LINBus, MI Bus, Microwire bus, MOST, MPI Bus, SMBus, SPI (Serial PeripheralInterface), USB, WiFi or other wireless Ethernet, optical fiber, Zigbee,IEEE 802.15.4, Rubee, Bluetooth, UWB (Ultra-Wideband), IrDA, or anyother suitable narrowband or broadband data communications technology. Acombination of various communications interfaces can be used withinand/or external to the inventory control system.

According to one embodiment, an operation cycle is as follows: theaccess control subsystem accepts user identification information. Thedata processing subsystem validates the user and sends a command viaEthernet and TCP/IP to activate image sensing devices, adjustilluminations for use by the imaging subsystem and unlock locks. Theimage sensing devices acquire images of the storage locations, andstitch the captured images together for evaluation of regions ofinterest related to objects. The imaging subsystem calculates attributesof the captured images and sends the attributes to the data processingsubsystem over the Ethernet communications network for furtherevaluations. The data processing subsystem evaluates the attributes anddetermines an inventory condition, updates records in the inventorydatabase and provides feedback to the user. Once the user signs off thesystem, the data processing subsystem sends a command via the Ethernetcommunications network to end the session, secure the drawers, and turnoff illuminations and image sensing devices in the imaging subsystem.

FIG. 3 shows an exemplary inventory control system implemented as a toolstorage system 300 according to this disclosure for storing tools.Storage system 300 includes a display 305, an access control device 306,such as a card reader, for verifying identity and authorization levelsof a user intending to access storage system 300, multiple tool storagedrawers 330 for storing tools. Tool storage system 300 includes an imagesensing device configured to capture images of contents or storagelocations of the system. The image sensing device may be lens-basedcameras, CCD cameras, CMOS cameras, video cameras, or any types ofdevice that captures images.

System 300 includes a data processing system, such as a computer, forprocessing images captured by the image sensing device. Images capturedor formed by the image sensing device are processed by the dataprocessing system for determining an inventory condition of the systemor each storage drawer. The term inventory condition as used throughoutthis disclosure means information relating to an existence ornon-existence condition of objects in the storage system.

The data processing system may be part of tool storage system 300; aremote computer having a data link, such as a wired or wireless link,coupled to tool storage system 300; or a combination of a computerintegrated in storage system 300 and a computer remote to storage system300. Detailed operations for forming images and determining inventoryconditions will be discussed shortly.

Drawers 330 are similar to those drawers 120 shown in FIG. 1 a. Display305 is an input and/or output device of storage system 330, configuredto display information to a user. Information entry may be performed viavarious types of input devices, such as keyboards, mice, voicerecognitions, touch panel entries, etc. Access control device 306 isused to limit or allow access to tool storage drawers 330. Accesscontrol device 306, through the use of one or more locking devices,keeps some or all storage drawers 330 locked in a closed position untilaccess control device 306 authenticates a user's authorization foraccessing storage system 300.

Access control device 306 may use one or more access authenticationmeans to verify a user's authorization levels, such as by using a keypad to enter an access code, a keycard reader to read from a key card orfob authorization level of a user holding the card or fob, biometricmethods such as fingerprint readers or retinal scans, or other methods.If access control device 306 determines that a user is authorized toaccess storage system 300, it unlocks some or all storage drawers 330,depending on the user's authorization level, allowing the user to removeor replace tools. In one embodiment, access to each storage drawer 300is controlled and granted independently. Based on an assignedauthorization or access level, a user may be granted access to one ormore drawers of system 300, but not to other drawers. In one embodiment,access control device 306 relocks a storage drawer 330 after a usercloses the drawer.

The location of access control device 306 is not limited to the front ofstorage system 300. It could be disposed on the top of the system or ona side surface of the system. In one embodiment, access control device306 is integrated with display 305. User information for authenticationpurpose may be input through display device with touch screen functions,face detection cameras, fingerprint readers, retinal scanners or anyother types of devices used for verifying a user's authorization toaccess storage system 300.

FIGS. 4 a and 4 b show a partial perspective view of an imagingsubsystem in tool storage system 300. As illustrated in FIG. 4 a, anaccess control device 306 in the form of a card reader is disposed on aside surface of the system. Storage system 300 includes an imagingcompartment 330 which houses an image sensing device comprising threecameras 310 and a light directing device, such as a mirror 312 having areflection surface disposed at about 45 degrees downwardly relative to avertical surface, for directing light reflected from drawers 330 tocameras 310. The directed light, after arriving at cameras 310, allowscameras 310 to form images of drawers 330. The shaded area 340 belowmirror 312 represents a viewing field of the imaging sensing device oftool storage system 300. Mirror 312 has a width equal to or larger thanthat of each storage drawer, and redirects the camera view downwardstoward the drawers. FIG. 4 e is an illustrative side view of system 300showing the relative position between cameras 310, mirror 312 anddrawers 330. Light L reflected from any of drawers 330 to mirror 312 isdirected to cameras 310.

FIG. 4 b is a perspective view identical to FIG. 4 a except that a coverof imaging compartment 330 is removed to reveal details of the design.Each camera 310 is associated with a viewing field 311. As shown in FIG.4 b, the combined viewing fields of cameras 310 form the viewing field340 of the image sensing device. Viewing field 340 has a depth of x. Forinstance, the depth of viewing field 340 may be approximately 2 inches.

FIG. 4 c is an alternative perspective view of tool storage system 300shown in FIG. 4 a, except that a storage drawer 336 now operates in anopen mode allowing partial access to its contents or storage locationsin storage drawer 336.

This arrangement of cameras 310 and mirror 312 in FIGS. 4 a-4 c allowscameras 310 the capability of capturing images from the top drawer tothe bottom drawer, without the need to substantially change its focallength.

In one embodiment, cameras 310 capture multiple partial images of eachstorage drawer as it is opened or closed. Each image captured by cameras310 may be associated with a unique ID or a time stamp indicating thetime when the image was captured. Acquisition of the images iscontrolled by a data processor in tool storage system 300. In oneembodiment, the captured images are the full width of the drawer butonly approximately 2 inches in depth. The captured images overlapsomewhat in depth and/or in width. As shown in FIG. 4D, the partialimages 41-45 taken by different cameras 310 at different points in timemay be stitched together to form a single image of the partial or entiredrawer and its contents and/or storage locations. This stitching may beperformed by the data processor or by an attached or remote computerusing off-the-shelf or custom developed software programs. Since imagesare captured in approximately two inch slices multiple image slices arecaptured by each camera. One or more visible scales may be included ineach drawer. The processor may monitor the portion of the image thatcontains the scale in a fast imaging mode similar to video monitoring.When the scale reaches a specified or calculated position, the dataprocessing system controls the image sensing device to capture andrecord an image slice. The scale may also assist in photo stitching.Additionally a pattern such as a grid may be applied to the surface thedrawer. The pattern could be used to assist the stitching or imagecapture process.

In another embodiment, the image sensing device includes larger mirrorsand cameras with wide angle lens, in order to create a deeper view fieldx, such that the need for image stitching can be reduced or entirelyeliminated.

In one embodiment, one or more line scan cameras are used to implementthe image sensing device. A line scan camera captures an image inessentially one dimension. The image will have a significant widthdepending on the sensor, but the depth is only one pixel. A line scancamera captures an image stripe the width of the tool drawer but onlyone pixel deep. Every time drawer 330 moves by a predetermined incrementthe camera will capture another image stripe. In this case the imagestripes must be stitched together to create a usable full drawer image.This is the same process used in many copy machines to capture an imageof the document. The document moves across a line scan camera and themultiple image stripes are stitched together to create an image of theentire document.

In addition to a mirror, it is understood that other devices, such asprisms, a combination of different types of lens including flat,concave, and/or convex, fiber optics, or any devices may direct lightfrom one point to another may be used to implement the light directingdevice for directing light coming from an object to a remote camera.Another option could be the use of fiber optics. The use of a lightdirecting device may introduce distortions into the captured images.Calibrations or image processing may be performed to eliminate thedistortions. For instance, cameras 310 may first view a known simplegrid pattern reflected by the light directing device and create adistortion map for use by the data process processor to adjust thecaptured image to compensate for mirror distortion.

For better image capture and processing, it may be desirable tocalibrate the cameras. The cameras may include certain build variationswith respect to image distortion or focal length. The cameras can becalibrated to reduce distortion in a manner similar to how the mirrordistortion can be reduced. In fact, the mirror calibration couldcompensate for both camera and mirror distortion, and it may be the onlydistortion correction used. Further, each individual camera may becalibrated using a special fixture to determine the actual focal lengthof its lens, and software can be used to compensate for the differencesfrom camera to camera in a single system.

In one embodiment, the image sensing device does not include any mirror.Rather, one or more cameras are disposed at the location where mirror312 was disposed. In this case, the cameras point directly down atstorage drawers 330. In another embodiment, each storage drawer 330 hasone or more associated cameras for capturing images for that storagedrawer.

Determination of Inventory Conditions

In an exemplary system 300, each storage location in drawer 330 isconfigured to store a pre-designated object, such as a pre-designatedtool. For instance, as shown in FIG. 2, each drawer 330 includes a foamlayer 180 having a plurality of tool cutouts 181 for storingpre-designated tools. Each tool cutout has a shape substantially similarto the outline of tool stored in the cutout. System 300 determines theexistence or absence of each object based on image data of one or morepre-defined regions of interest (ROI). A region of interest (ROI) is apre-defined area whose image data includes useful information indetermining the existence or absence of an object at a specific storagelocation. An ROI may be defined by system designers and may cover oroverlap part of or the entire tool cutout, or does not cover or overlapa tool cutout at all. System 300 determines an inventory of objects ortools by analyzing image data of the pre-defined ROIs. Details for usingROIs for determining an inventory condition will be discussed shortly.

In order to improve the efficiency in image data processing andtransmissions, system 300 determines the presence or absence of tools indrawers 330 based on image signatures derived based on captured imagesof one or more ROIs and reference image signatures of the ROIs. An imagesignature is a representation of a specific image or ROI includingattributes unique to each image or ROI. These unique attributes includeone or more of color, size, shape, width, height, depth, silhouette orprofile, specific gravity, weight, diameter, reflectivity, density,thermal properties, viscosity, dimensions, texture, surface finish, etc.Representations of these attributes, according to one embodiment, arederived based on one or more captured images of an area. Details ofderiving image signatures for a specific image or ROI will be describedshortly.

System 300 has access to signature reference data which includesreference image signatures of the same area or ROI when an object, suchas a tool, is present, and when the object is missing. For each area orROI, a reference image signatures may be generated by taking an image ofthat area or ROI both when an object is present and not present.

Based on a correlation of image signatures for an image of an area orROI and the signature reference data, system 300 determines whether anobject corresponding to a storage location is present or missing. Forinstance, two sets reference image signatures are used one related to ancutout being occupied by a corresponding tool, and one related to thesame cutout not being occupied by the corresponding tool. Adetermination of an inventory condition is made based on a correlationof image signatures of a captured image of an area of covering thecutout and an area immediately surrounding the cutout, and signaturereference data of the same area.

Suitable software may be executed by one or more data processors insystem 300 or an attached computer for performing inventorydeterminations based on captured images. A non-volatile memory device ofsystem 300 stores information identifying a relationship between eachknown storage location in the drawer and its correspondingpre-designated object. The memory device also stores pre-defined ROIscorresponding to each tool and reference image signature information inconnection with the ROIs or the tools. One or more data processorsdetermines which storage locations in a drawer are occupied by theircorresponding pre-designated tool and which storage locations in thedrawer are not occupied by their corresponding pre-designated tools.Identity of one or more existing and/or missing tools are determinedbased on the stored relationship identifying each storage locations andtheir corresponding pre-designated objects. The system also determineswhether a tool is out of place or the wrong tool is in the storagelocation.

Details of image recognition and inventory determination are nowdescribed.

An exemplary tool storage system according to this invention utilizes aunique approach to perform image recognition, which dramatically reducesthe amount of needed data and requires scientifically less computingpower and time to perform needed calculations.

(1) Creation of Reference Data

As discussed earlier, exemplary system 300 determines an inventorycondition of tools based on signatures derived based on captured imagesand reference image signatures of tools and tool cutouts. Thisdisclosure proposes an efficient and unique approach in generating filesof the reference image signatures.

In order to properly effectively and efficiently process images capturedby cameras, system 300 needs to have access to information describingreference data including reference image signatures of pre-defined ROIsin connection with each tool; and information including identities oftools, locations of the tools, cutout locations in each drawer, cutoutprofiles, definitions of tool silhouettes, definitions of fingercutouts, channels between tool silhouettes, definitions of positions oftools in each drawer relative to a known feature of the foam, tooldescriptions, part numbers and any information that is useful inperforming image recognition. The information is collectively referredto as reference data.

An exemplary system according to this disclosure generates referencedata in a highly automated manner by using existing data files calledcutout files. Cutout files are created during the manufacture of foamlayers with tool profile cutouts for use in tool storage systems. Thesecutout files are typically in an industry standard format such as .dxfformat, and include information of tool profile cutouts for each drawerand a relative position of each tool in the drawer. Cutout files used inthe creation of the drawer foam layers also provide additional usefulinformation including the drawer where each tool is being stored, partnumbers of the tools, National Serial Numbers, manufacturer partnumbers, tool serial numbers, tool descriptions, etc.

For example, a software program called True Fit by Snap-on Incorporatedincludes a database of tool silhouettes and data related each tool. Whenmaking foam layers 180 with tool cutouts 181 for use in tool storagesystems, a foam layout technician creates a foam layout for each drawer330 according to a master tool list which includes all the tools to beincluded in tool storage systems for a specific order or customer. Thelist may be created by sales representatives and/or customers. The TrueFit program, when executed by a computer, generates cutout filesincluding foam layouts for each drawer 330 based on the master tool listand pre-stored data regarding each tool in a database. The cutout fileincludes information of each cutout, such as shapes and sizes, list oftools, and positional information of each cutout in each drawer. In oneembodiment, the positional information includes coordinate informationof each cutout relative to a common coordinate system of each drawer.The technician then cuts foams according to the foam layout by using a2D water jet cutter or 3D machining tool.

By adapting cutout files used in foam development, such as .dxf files,to reference data for use in image recognition, the tool storage systemnow has access to data relating to the exact position of each tool ineach drawer, along with the shape of the cutouts and other usefulinformation such as tool number, serial number etc. This processeliminates the requirement to manually enter each tool into theinventory of the tool storage system, and it also provides preciseinformation about the location of each tool in each drawer and theassociated data for each tool. Additional information derived from thisprocess may include reference image signatures and other toolattributes, such as color, size, shape, area, centroid, area moment ofinertia, radius of gyration, specific dimensions (such as wrenchopening, socket diameter, screwdriver length, etc.). Therefore, when thereference data is loaded, attributes for each tool are also accessibleby the tool storage system.

(2) Regions of Interest

In order to improve efficiency of data processing, for each captureddrawer image, exemplary system 300, rather than processing the entireimage, performs image recognition focusing on pre-defined regions ofinterest (ROI), which are subsets of the captured image and henceinclude far less data required for processing. According to oneembodiment, each tool has one or more pre-defined ROIs As discussedearlier, an ROI may cover or overlap part of or the entire tool cutout181, or does not cover or overlap tool cutout 181 at all.

Area of interests may be described by one or more of cutout locations ina coordinate system, cutout profiles, definitions of tool silhouettes,definitions of finger cutouts, channels between tool silhouettes,definitions of positions of tools in each drawer relative to a knownfeature of the foam, tool descriptions, part numbers and any informationthat is useful in performing image recognition.

(3) Training Process

A computerized training process is performed to adapt the cutout filesinto reference data useful by an exemplary tool storage system.Exemplary steps of the training process are described below:

a. Read Tool Information from Foam Cutout File

This step reads and loads a cutout file which contains tool dataincluding tool positions, silhouettes, foam colors, background colors,attributes and other data.

This step also scales the image both horizontally or vertically toconvert from the dimensional format used in the creation of the cutoutfile to pixels which are used in the camera and image based system.Typically there are about 8 to 10 pixels per point. A softwareconversion file with appropriate coding is used to make this conversion.

b. Create Tool Location Data Structure

ROI's are determined using the foam layout by processing areas relatedto shapes and positions of tool cutouts. A corner of the overall imageis defined as an origin. A position of a bounding box within the imageis then determined. The ROI is inside the bounding box and is determinedby row and column number inside the bounding box. Any pixel within thebounding box that contains a portion of the ROI is detailed as orderedpairs with the top left corner of the bounding box as the origin. Anexemplary data structure of ROI is provided below:

Drawer.s1.d1 // Drawer name 96 // resolution Silhouette TMCO16  // Partnumber TOOL_BOX_ID 1051  // Tool box id DRAWER 01  // Drawer No POSITIONS-0058 // Position DESCRIPTION Wrench. Crowfoot, // Description OpenEnd, 1/2″ ULC 1586 2379 // Bounding box (Upper Left Corner) LRC 16872499 // Bounding box (Lower Right Corner) 1 22 58 // pixel data (Withinthe bounding 1 21 60 box by number of discreet 2 20 62 95 20  sectionswithin the row, offset 2 19 63 96 22  and width (# pixels wide) 2 19 6495 20 1 18 66 1 17 68 1 16 70 1 16 70

c. Determine which Camera Processes the Tool

In an exemplary tool storage system that uses 4 cameras or six camerasfor capturing drawer images, each camera has a 40 degree wide field ofview (FOV), with 20 degrees of the FOV on either side of the center lineof the image. The images from camera to camera can overlap. Depending onthe position of the drawer, the overlap may be small as in the topdrawer, or large, as in the bottom drawer where each camera can see theentire drawer. The tools and their associated ROI's are assigned to thecameras by determining the position of the centroid of each tool ROI andassigning the respective ROI to a camera having a FOV centerline closestto the ROI.

If an ROI expands into converges by more than one camera, the ROI isdivided into pieces and assigned to individual cameras as outlinedabove.

d. Create Dilation Zone and Buffer Zone for Tools

In certain situations, a captured image related to a tool may deviatefrom the reference data, even when the tool is placed in itscorresponding cutout or storage location. For instance, a tool mayoverhang the edge of the tool cutout, or may be pressed tightly into thefoam causing the form to compress or deform, or the tool cutout may notbe in the exact position which was expected. These situations, if notproperly addressed, may cause a tool storage system to trigger falsealarms.

To address these issues, an exemplary system utilizes unique image datato evaluate an inventory condition of a tool. As shown in FIG. 5, adilation zone and a buffer zone are assigned around each tool cutout.The buffer zone is an annular ring created around the tool cutout andincludes an adjustable width, such as a number of pixels. For example, abuffer zone for a tool cutout may be 5 pixels expanding away from theboundary of the tool cutout. The width is arbitrary and depends onsystem design. The dilation zone is an annular ring around the bufferzone and may be assigned an adjustable number of pixels. For example,the buffer zone may be expanded by 5 pixels away from the outer boundaryof the buffer zone. The width is arbitrary and depends on system design.

In one embodiment, image data related to the buffer zone is ignored inthe evaluation of the image, while image data related to the dilationzone is evaluated for attributes such as color, intensity, hue andsaturation, etc. This data will be used later in evaluation ofsituations where something is laying over the edge of the tool cutoutindicating a wrong tool has been placed in the storage location orcutout, or the right tool has been improperly placed in the storagelocation or cutout and is out of position, or there is somethingcovering at least part of the buffer zone. A warning to the user andadministrator will be issued when this occurs.

4. Image Recognition Based on Image Signatures

Each image includes attributes that represent unique characteristics ofthe image. These attributes include color, size, shape, width, height,depth, silhouette or profile, specific gravity, weight, diameter,reflectivity, density, thermal properties, viscosity, dimensions,texture, surface finish, etc. An exemplary system according to thisinvention utilizes a unique approach to extract informationrepresentative of each ROI from the captured image. This extractedinformation uniquely describes attributes of the ROI and is referred toas an image signature of the ROI.

As discussed earlier, a non-volatile storage device in system 300 storesdata related to pre-defined ROI whose image data require processing bysystem 300. A pre-stored look-up table may be to identify each tool, itscorresponding cutout information, and pre-selected ROIs whose image dataare useful in determining an inventory condition of the tool. Aninventory condition with respect to a tool is determined based on theimage signature of each ROI associated with the tool which is derivedbased on the captured images and reference image signatures. Since onlythe image signature, not the raw image data, is used in determining aninventory condition, the efficiency in signal processing and datatransmission is significantly improved.

The process for generating an image signature for an image is nowdescribed. One sample image signature using image hue and saturation isdescribed. Other signatures that use various image attributes can bedevised to which will can also be used to determine presence, absence,or identification of items in the inventory system.

An exemplary algorithm for the process is provided below:

Conversion of RGB (red, green, blue) to IHS (intensity, hue, saturation)I=(r+g+b)/3if r, g and b are all equal, then h and s=0elseLet:u=(2*r)−g−bv−=sqrt(3)*(g−b)Then:h=arctan(v/u)if (h<0), add 1 to h, so 0<=h<<=1s=sqrt (u²+v²)/(Max saturation for this intensity and hue);The relevant structures are:

typedef struct {    BYTE hue;    BYTE saturation;    BYTE intensity;   BYTE percent; } PP_COLOR; typedef struct {    BOOL    partialRoi;   PP_COLOR  myColors[5];    PP_COLOR  foam[5]; } PP_SIGNATURE;

The exemplary system processes pixel data in ROI's corresponding to eachtool cutout and its surrounding dilation zone. The RGB value of eachpixel is converted to an IHS (intensity, hue, saturation) value. Anexemplary system determines distribution characteristics of imageattributes of the captured image. For example, a histogram describing apixel number distribution with respective to various image attributeslike intensity, hue and/or saturation values, is created.

In one embodiment, for image pixels with intensity values below andabove pre-defined low and high thresholds (for example, 0.2 and 0.8,respectively), their hue and saturation are ignored as being unreliableand set to zero. These pixels are accumulated in two separate bins.

For pixels with intensity values between the threshold values, theirintensity values are ignored because they too easily influenced byvariable illumination intensity factors. For these pixels, the hue andsaturation values are accumulated in the bins of a 2D (hue-saturation)histogram. Each bin represents a set of preset values of hue andsaturation on the histogram.

The 2D histogram is then examined based on pixel number distributions tofind the bin with the most pixels. Bins adjacent to the bin with themost pixels are included until the pixel count of a bin falls below apredefined fraction of the pixel count in the peak bin. For instance,the predefined fraction may be set as 0.5. The average intensity, hueand saturation of the selected bins is calculated and entered into thebytes of the PP_COLOR structure.

The percent member of the PP_COLOR structure is the percent of all thepixels in the ROI that were in this group of bins. The bins in thisgroup are then ignored in any further processing, and the process isrepeated to find the next such group of bins. This process is repeateduntil a predetermined number of bin groups are found.

The number of pixels in the two separate low and high intensity bins isalso examined at each step, and these bins may account for one of thePP_COLOR structures in the PP_SIGNATURE structure.

In this manner, the structure of an image signature of ROI(PP_SIGNATURE) is filled in, with descriptive data for the five mostpopulous pixel color groups. The PP_COLOR structure for the dilationzone around the tool in the PP_SIGNATURE structure is computed in likemanner. The use of five colors for the signature is only an example andany number colors could be used for the signature.

Similar process is performed for the following baseline images:

Tool cutouts/No tool in ROI;

Tool cutouts/tool in ROI; and

Dilation zone.

Reference image signatures are acquired once, as part of a setupprocedure. Reference signatures may be acquired for each tool cutoutwhen the correct tool is known to be present, for each tool cutout whenthe tool is known to be absent, for the dilation zone when the correcttool is know to be present, and for the dilation zone when the tool isknown to be absent. Then in normal operation the system acquiressignature data for an ROI from a captured image and compares it to thetool-known-to-be-not-present data, the tool-known-to-be-present data.Reference image signatures can relate only to a particular tool and notto the drawer of tools. The reference image signatures can be generatedindependently for each tool and stored in a database. The is generallydone by capturing and images of a tool cutout with and without the toolin place. The image of just the tool and tool cutout is processed toextract the reference image signatures for all defined conditions oftool presence. The reference image signatures can be extracted from thedatabase and incorporated in the inventory management system so that noreference image of the actual tool drawer is ever required. Further, thereference image signatures can be generated without ever capturing anyimages. Known parameters of the foam, tool cutout, and the associatedtool such as colors and dimensions are manually or programmatically usedto generate image attributes that define the reference image signaturesfor the tool cutout and tool.

An inventory condition related to an ROI is determined based on imagesignatures of the ROI's in the captured image and the reference imagesignatures.

If the image signature of a tool cutout ROI matches the reference imagesignature when the tool is not present within a preset percentagetolerance, then the tool is determined to be missing from the ROI andthus is issued or in use. One the other hand, if the image signature ofa tool cutout ROI matches the reference image signature when the tool ispresent within a preset percentage tolerance, then the item isdetermined to be present in the ROI. The percentage tolerance used fordetermining tool presence or absence is adjustable. A suitable value maybe selected empirically according to a design preference.

Examination of the image signature of the dilation zone ROI incomparison to reference image signatures corresponding to the dilationzone ROI can be used to determine conditions beyond just whether a toolis present or missing. For example, if a tool is not placed in itscorresponding cutout correctly or the wrong tool is placed in the toolcutout, part of the tool may be outside of the cutout and in thedilation zone. Additionally an unexpected object such as a shop rag orextra tool could be placed in the drawer and be blocking part of adilation zone of a tool cutout. The signature of the dilation zone ROIwould not match its reference image signature, and the system woulddetermine that an unknown condition exits in the storage compartment. Inthis case an alert could be generated to indicate the relevant storagelocation should be manually checked to correct the anomaly.

If the image signature of an ROI does not match either reference imagesignatures within a preset percentage tolerance, then an error orexception is determined to have taken place and an alert is given to theuser and system administrator through the PC and GUI systems of theequipment. The error alert could be for wrong tool, misplaced tool,unknown item across multiple ROI's etc. In one embodiment the signatureof the item in the tool cutout ROI could be compared to a database ofall tools in the drawer, in the storage cabinet, or known globally. Fromthis comparison the particular tool stored in the tool cutout can bedetermined and reported to the inventory system. In a broad sense thistechnique can be used to identify all of tools in a drawer withoutknowing which tools should be in the drawer or where there should beplaced.

In one embodiment, each image signature is treated a vector. Similaritybetween the image signature of the ROI corresponding to the capturedimage and the reference image signature of the same item when the toolis known to be not present is calculated. For example, the similaritymay be determined based on a distance between two vectors computed inknow manners. Also, similarity between the image signature of the ROIcorresponding to the captured image and the reference image signature ofthe same item when tool is known to be present is computed. A tool isdetermined to be not present if the similarity level between the imagesignature of the ROI corresponding to the captured image and thereference image signature of the same item when tool is known to be notpresent is higher than similarity level between the image signature ofthe ROI corresponding to the captured image and the reference imagesignature of the same item when tool is known to be present. Otherwise,the tool is determined to be present.

In another embodiment, each of the top most populous pixel groups in theimage signature of the ROI corresponding to the captured image iscompared with its counterpart of the reference image signature of thesame item when tool is known to be not present, the reference imagesignature of the same item when tool is known to be present,respectively. A GOF (Goodness of Fit) value is determined from thesignature matches and percentages. For example, the GOF value may bedefined as an accumulation of min (percentage_of captured_ROI,percentage_of_reference) corresponding to each entry in the signature.The accumulated percentage is then compared with a threshold value. Ifthe GOF value with respect to the reference signature when tool ispresent is above the threshold value, it is determined that the tool ispresent. If the GOF value with respect to the reference signature whentool is not present is above the threshold value, it is determined thatthe tool is not present. If both the GOF values with respect to thereference signature when tool is present is above the threshold valueand the reference signature when tool is not present are below thethreshold value, it is categorized as a peculiar condition and a warningsignal will be triggered or lodged. Such peculiar condition may becaused by an overhung tool, a deformed cutout, or wrong tool placed inthe cutout.

Occasionally, a significant portion of a tool's attributes may match theattributes of the background and make detection of presence or absencemore difficult. In these cases, the item can be split into multipleROI's. The areas with ROI attributes matching the background will beignored and only those areas with ROI attributes different from theattributes of the background will be evaluated.

A look-up table of image reference signatures corresponding to eachinventory item may be created during a manufacture process and preloadedonto the tool storage system. At system setup, a complete database ofreference image signatures can be loaded into each system along with theROIs, part numbers and descriptions, and other data in the tooldatabase.

Another feature of an exemplary tool storage system according thisdisclosure is automated item addition/deletion. This is a processwhereby the system is configured to recognize new tools and add them tothe database or remove tools that are no longer needed or useful fromthe database.

The same type of data files used to create the foam cutouts andreference data are used in the modification of existing drawer layoutsto add or delete tools. The file is then loaded to the memory of thesystem and the foam is changed. The system automatically recognizes thatthere is a new tool(s) in (or removed from) the database and thespecified storage location or cutout is now in place (or removed).

It has been determined that the precise location of an ROI correspondingto the image and its associated tool cutout in the foam may varyslightly from the position as calculated in the tool training andloading files. In order to compensate for this possible difference inactual and calculated positions, holes are provided in the foam layer asa position check. These hole positions are related to the position ofthe drawer tracking and positioning strips and provide verification ofthe actual positions of the ROI's and tool cutouts in the drawer foam.

Another embodiment according to this disclosure utilizes speciallydesigned identifier for determining an inventory condition of objects.Depending on whether a storage location is being occupied by an object,an associated identifier appears in one of two different manners in animage captured by the image sensing device. For instance, an identifiermay appear in a first color when the associated storage location isoccupied by a tool and a second color when the associated storagelocation is unoccupied. The identifiers may be texts, one-dimensional ortwo-dimensional bard code, patterns, dots, code, symbols, figures,numbers, LEDs, lights, flags, etc., or any combinations thereof. Thedifferent manners that an identifier may appear in an image captured bythe image sensing device include images with different patterns,intensities, forms, shapes, colors, etc. Based on how each identifierappears in a captured image, the data processor determines an inventorycondition of the object.

FIG. 6 a shows an embodiment of identifier designs. As shown FIG. 6 a,storage location 511 is designated to store tool 510, and storagelocation 52 is currently occupied by its designated tool 520. Storagelocation 53 is not occupied by its designated tool. Each storagelocation 51, 52, 53 has an associated identifier. Depending on whethereach storage location 51-53 is being occupied by a corresponding tool,each identifier appears in an image captured by cameras 310 in one oftwo different manners. For example, each identifier may not be viewableby cameras 310 when a corresponding tool is stored in the respectivestorage location, and becomes viewable by cameras 310 when an object isnot stored in the respective storage location. Similarly, a differentembodiment may have an identifier viewable by the image sensing devicewhen an object is stored in the respective storage location, and is notviewable by the image sensing device when an object is not stored in therespective storage location.

For instance, the bottom of storage locations 51-53 includes anidentifier made of retro-reflective material. Since storage locations 51and 53 are not occupied by their respective designated tools, theirassociated identifiers 511 and 513 are viewable to the image sensingdevice. On the other hand, storage location 52 is currently occupied byits designated tool, its identifier is blocked from the view of theimage sensing device. When the particular tool is stored in the storagelocation, the identifier is blocked from the view of the image sensingdevice and not viewable by the image sensing device. On the other hand,if the storage location is not occupied by the particular tool, theidentifier is viewable by the image sensing device and shows up as ahigh intensity area on an image of the drawer. Accordingly, a highintensity area represents a missing tool. The system 300 detectslocations with missing tools and correlates the empty locations withstored relationship identifying each storage locations and theircorresponding tools. The system 300 determines which tools are not intheir specified locations in a drawer. It is understood that theidentifiers may be implemented in many different manners. For instance,the identifiers may be designed to create a high intensity image when astorage location is occupied and an image with less intensity when thestorage location is occupied.

In one embodiment, each identifier is implemented with a contact sensorand an LED. As shown in FIG. 6 b, storage location 61 is associated witha contact sensor 62 and an LED 63. When contact sensor 61 senses a toolis in storage location 61, a signal is generated by contact sensor 61and controls to turn off power supply to LED 63. On the other hand, ifcontact sensor 62 detects that a tool is not in storage location 61,control sensor 62 generates a control signal which controls to turn onLED 63, which creates a high intensity area in an image captured by theimage sensing device. Each high intensity area in an image indicates astorage location without an associated tool. The system 300 identifiesremoved or missing tools by determining which storage locations are notoccupied by tools and pre-stored information identifying correspondingtools of the locations. In still another embodiment, the identifier isunique to the pre-designated tool stored in each respective storagelocation. The data processor is configured to determine an inventorycondition by evaluating whether at least one viewable identifier existsin an image of the storage locations captured by the image sensingdevice, and pre-stored relationship between each pre-designated objectand a respective identifier unique to each pre-designated object.

In still another embodiment, an identifier associated with a storagelocation creates a high intensity image when the storage location isoccupied, and a lower intensity image when the storage location isunoccupied. The system 300 determines which tools exist based ondetected identifiers and pre-stored information identifying arelationship between each storage location and the correspondingpre-designated object. In another embodiment, the identifier is uniqueto a pre-designated object stored in each respective storage location.The system 300 determines an inventory condition of existing objects byevaluating identifiers that exist in an image of the storage locationscaptured by the image sensing device, and pre-stored relationshipbetween each pre-designated object and a respective identifier unique toeach pre-designated object.

In still another embodiment, each object stored in the system 300includes an attached identifier unique to each object. The dataprocessor has access to prestored information identifying each toolstored in the system and known information identifying a relationshipbetween each object and a respective identifier unique to eachpre-designated object. The data processor determines an inventorycondition of objects by evaluating identifiers that exist in an image ofthe storage locations captured by the image sensing device, and therelationship between each pre-designated object and a respectiveidentifier unique to each pre-designated object. For instance, system300 stores a list of tools stored in the system and their correspondingunique identifiers. After cameras 310 captures an image of a storagedrawer, the data processor determines which identifier or identifiersare in the image. By comparing the identifiers appearing in the imagewith list of tools and their corresponding unique identifiers, the dataprocessor determines which tools are in the system and which ones arenot.

As discussed earlier, identifiers associated with the storage locationsmay be used to determine which locations have missing objects. Accordingto one embodiment, system 300 does not need to know the relationshipbetween each storage location and the corresponding object. Rather, eachidentifier is unique to a corresponding object stored in the storagelocation. The data processor of system 300 has access to pre-storedinformation identifying a relationship between each identifier and thecorresponding object, and information identifying each object. In otherwords, system 300 has access to an inventory list of every object storedin system 300 and its respective unique identifier. When an empty toolstorage location is detected by system 300, the corresponding identifieris extracted from the image and decoded by system software. As eachidentifier is unique to a corresponding object, system 300 is able todetermine which object is missing by checking the relationship betweeneach identifier and the corresponding object, and the inventory list ofobjects. Each identifier unique to an object stored in a storagelocation may be disposed next to the storage location or in the storagelocation. In one embodiment, the identifier is disposed next to thestorage location and is always viewable to the image sensing device nomatter whether the location is occupied by an object or not. In anotherembodiment, when an identifier is disposed in the correspondinglocation, the identifier is not viewable to the image sensing devicewhen the location is occupied by an object, and is viewable to the imagesensing device when the location is not occupied by an object.

An embodiment of this disclosure utilizes combinations of baselineimages and identifiers unique to objects to determine an inventorystatus. For example, a baseline image may include information of astorage drawer with all storage locations occupied with their respectivecorresponding objects, wherein each storage location is associated withan identifier unique to an object stored in the storage location. Aninventory condition is determined by comparing an image of the storagelocations and the baseline image, to determine which locations areoccupied by objects and/or which locations have missing objects.Identifications of the missing objects are determined by identifying theidentifier associated with each storage location with missing object.

Another embodiment of this disclosure utilizes unique combinations ofidentifiers to determine an inventory status. For instance, each storagelocation may have a first type of identifier disposed in the locationand a second type of identifier unique to an object stored in thestorage location and disposed next to the storage location. The firsttype of identifier is viewable to an image sensing device when thelocation is not occupied by an object and not viewable by an imagesensing device when the location is occupied by an object. The firsttype of identifier may be made of retro-reflective material. If astorage location is not occupied by an object corresponding to thestorage location, the identifier of the first type is viewable by theimage sensing device and shows up as a high intensity area. Accordingly,each high intensity area represents a missing object, which allowssystem 300 to determine which locations having missing objects. Based onidentifiers of the second type associated with those locations withmissing objects, system 300 identifies which objects are missing fromsystem 300. Consequently, an inventory condition of system 300 isdetermined.

According to still another embodiment, system 300 uses image recognitionmethods to identify an object missing from system 300. System 300 hasaccess to an inventory list indicating which tools are stored in eachdrawer or system 300. However, system 300 does not have to know wherethe tools are stored. The tools are placed in foam cutout locationsspecific for each tool. Using characteristics such as size, shape,color, and other parameters image recognition software identifies eachtool in the drawer. Missing tools are simply the tools on the inventorylist that are not identified as being in the drawer.

System 300 records access information related to each access. The accessinformation includes time, user information related to the access,duration, user images, images of storage locations, store unit orcontents of the storage system, objects in the storage system, etc., orany combinations thereof. In one embodiment, system 300 includes a usercamera that captures and stores image of the person accessing storagesystem 300 each time access is authorized. For each access by a user,system 300 determines an inventory condition and generates a reportincluding associating the determined inventory condition with accessinformation.

Timed Image Capturing

Embodiments of this disclosure utilize uniquely timed machine imaging tocapture images of system 300 and determine an inventory condition ofsystem 300 according to the captured images. In one embodiment, system300 activates or times imaging of a storage drawer based on drawerlocations and/or movements, in order to create efficient and effectiveimages. For instance, a data processor of the system 300 uses drawerpositions to determine when to take overlapping partial images asdiscussed relative to FIGS. 4 a-4 e, to assure full coverage of a drawerbeing accessed by a user. In another example, drawer positioninformation may be useful to the stitching software in the constructionof a full drawer image. Drawer position information may be used to helplocate the positions of the cutouts in the drawer.

In one embodiment, the data processor of system 300 controls the imagesensing device to form images of a drawer based on a pre-specifiedmanner of movement by the drawer. For instance, for each access, system300 only takes images of the drawer when it is moving in a specifiedmanner or in a predetermined direction. According to one embodiment, theimage sensing device takes images when a drawer is moving in a directionthat allows decreasing access to its contents or after the drawer stopsmoving in the direction allowing decreasing access to its contents. Forexample, cameras may be controlled to take pictures of drawers when auser is closing a drawer, after a drawer stops moving in a closingdirection or when the drawer is completely closed. In one embodiment, noimages are taken when the drawer is moving in a direction allowingincreasing access to its contents, such as when a drawer moves from aclose position to an open position.

Locations, movements and moving directions of each storage drawer may bedetermined by using sensors to measure location or movement sensorsrelative to time. For instance, location information relative to twopoints in time may be used to derive a vector indicating a movingdirection.

Examples of sensors for detecting a position, movement or movingdirection of storage drawers include a sensor or encoder attached to adrawer to detect its position relative to the frame of system 300; anon-contact distance measuring sensor for determining drawer movementrelative to some position on the frame of the system 300, such as theback of the system 300; etc. Non-contact sensors may include optical orultrasonic sensors. A visible scale or indicator viewable by cameras 310may be included in each drawer, such that camera 210 could read thescale to determine drawer position.

A change in an inventory condition, such as removal of tools, occurringin the current access may be determined by comparing inventoryconditions of the current access and the access immediately before thecurrent access. If one or more objects are missing, system 300 maygenerate a warning signal, such as audible or visual, to the user,generate a notice to a remote server coupled to system 300, etc.

In another embodiment, the image sensing device is configured to formimages of the storage locations both when storage drawer 330 moves in adirection allowing increasing access to its contents, and when storagedrawer 330 subsequently moves in a direction allowing decreasing accessto its contents. For example, when a user opens drawer 330 to retrievetools, the moving direction of drawer 330 triggers cameras 310 tocapture images of drawer contents when it moves. The captured image maybe designated as a “before access” image representing a status before auser has accessed the contents of each storage drawer. An inventorycondition is determined based on the captured images. This inventorycondition is considered as a “before access” inventory condition.Cameras 310 stops capturing images when drawer 330 stops moving. Whenthe user closes drawer 330, the moving direction of drawer 330 triggerscameras 310 to capture images of drawers 330 again until it stops orreaches a close position. An inventory condition of the drawer isdetermined based on images captured when the user closes drawer 330.This determined inventory condition is designated as an “after access”inventory condition. A difference between the before access inventorycondition and the after access inventory condition indicates a removalor replacement of tools. Other embodiments of this disclosure controlcameras to take the “before access” image before a storage drawer isopened or after the storage drawer is fully opened or when its contentsare accessible to a user. According to another embodiment, the imagesensing device is timed to take an image of each drawer 330 when it wasdetected that access by a user is terminated. As used herein in thisdisclosure, terminated access is defined as a user no longer havingaccess to any storage locations, such as when drawer 330 is closed orlocked, when door 250 is closed or locked, etc., or an indication by theuser or the system that access to the storage system is no longerdesired, such as when a user signs off, when a predetermined period oftime has elapsed after inactivity, when a locking device is locked by auser or by system 300, etc. For each access, a position detector orcontact sensor is used to determine whether drawer 330 is closed. Afterthe drawer is closed, the image sensing device captures an image ofdrawer 330. The data processing system then determines an inventorycondition based on the captured image or images. A difference in theinventory condition may be determined by comparing the determinedinventory condition of the current access to that of the previousaccess.

According to another embodiment, for each access, the image sensingsystem 300 is timed to capture at least two images of drawer 300: atleast one image (initial image) captured before a user has access tostorage locations in drawer 300 and at least one image captured afterthe access is terminated, as discussed earlier. The initial image may betaken at any time before the user has access to the contents or storagelocations in the drawer. In one embodiment, the initial image iscaptured after a user requests access to system 300, such as by slidinga keycard, punching in password, inserting a key into a lock, providingauthentication information, etc. In another embodiment, the initialimage is captured before or in response to a detection of drawermovement from a close position or the unlock of a locking device ofsystem 300.

The data processing system of system 300 determines an inventorycondition based on the initial image, and assigns the determinedinventory condition as “before access” inventory condition; anddetermine an inventory condition based on the image captured after theaccess is terminated and designated the determined inventory conditionas “after access” inventory condition. A change in the inventorycondition of objects in system 300 may be determined based on acomparison of the “before access” and “after access” inventoryconditions or a comparison of the initial image and the image capturedafter the access is terminated.

Concepts and designs described above may be applicable to other types ofstorage systems, such as a type shown in FIG. 1B, where a single doorcontrols the access to multiple shelves or drawers. In one embodiment,the image sensing device may be timed to capture images of the storagelocations after a detected termination of access, such as closing door250, locking door 250, signing out, etc. It is understood that varioustypes of sensors, such as contact sensors, infrared sensors, may be usedto determine when a door is closed. Similar to the discussions earlier,the image sensing device captures images of the storage locations, anddetermine an “after access” inventory condition based on the capturedimage. A change in the inventory condition related to the access bycomparing an inventory condition of the current access and that of thelast access. According to another embodiment, the image sensing deviceis timed to take “before access” images of the storage locations beforea user has access to the storage system. For instance, the cameras maybe timed to capture images of the storage locations after a userrequests access to the system, after detecting an opening of door 250,after receiving authentication information from a user, etc. The storagesystem determines a “before access” inventory condition based on the“before access” image. A change in the inventory condition may bedetermined according to a difference between the “before access” and“after access” inventory conditions, or a difference between the “beforeaccess” and “after access” images.

Image Calibration

As discussed earlier, multiple cameras may be used to capture partialimages of a drawer. Such images are then stitched together to form acomplete image of a drawer for further processing. Accordingly, for thepurpose of stitching images, it is important to know the relativepositional relationships between the partial images such that thepartial images can be stitched or combined properly. Furthermore,processing of an image of an ROI may be performed based on a partialimage as long as the entire ROI is within the partial image. Under suchcircumstance, it is important for a tool storage system to know locatethe correct location of an ROI from a partial image, especially thepartial image may be distorted, twisted or skewed during the capturingof the image.

An embodiment of this disclosure provides known, non-repeating referencepatterns in each drawer. For example, as shown in FIG. 6 a, a drawer isprovided with two visible, non-repeating scales A, B, C . . . S on bothsides of a drawer. Information of the relative positions of the scalesin the drawer is known and stored in the system. At any given time whenan image is captured, patterns of both scales must always appear in thecaptured image. Only one camera or multiple cameras can be used tocapture the images. As the width of the drawer gets wider one can use acamera with a shorter focus length. However, such camera with a shorterfocus length would reduce the resolution of the image. Alternatively,more than one camera separated or angled can be used to acquire thedesired drawer width and resolution. It is possible to use one or morecameras dedicated to viewing the scale pattern, or to use the samecamera that will view all or part of the drawer contents.

If, as shown in FIG. 6 b, a partial image of the drawer shown in FIG. 6a is obtained, any ROI (region of interest) within the partial imageshown in FIG. 6 b may be determined by interpolation of known toolcutout reference position information within the known foam dimensionsand images of the scale information. From the visible scale in FIG. 6 b,it is easily recognized where the partial image is in the full drawerimage shown in FIG. 6 a. By knowing where the partial image is relativeto the entire drawer, the partial image can be properly stitchedtogether with other partial images.

Also, the ratio of a to b to c in the partial image shown in FIG. 6 b isthe same as the ratio x to y to z of the stored cutout file informationas shown in FIG. 6 a. With this known ratio, the location of any ROIcorresponding to the partial image in FIG. 6 b can be determined.

Furthermore, with two or more reference patterns or scales that canalways be captured by the camera at any given time, even if theperspective of the image changes, using the relative positions of thereference patterns or scales in the captured image and the knownrelationships of the drawer, the distorted image may be rescaled,rotated or adjusted to convert the distorted image back to fit the knownrelationships. For example, if a distance between the two scales in FIG.6 b is reduced, the system is able to determine an expansion factor bycalculating a ratio between a distance between the scales in thecaptured image and the known distance stored in the system. Thisexpansion factor is then applied to the captured image to convert theimage back to normal.

Furthermore, by knowing a position of each partial image relative to theentire drawer, a moving speed of a drawer can be determined. Forexample, as shown in FIG. 6 c, two partial images 1 and 2 are captured.As discussed earlier, based on images of the scales, the system candetermine where the partial image is relative to the entire drawer byreferring to the reference data stored in the system. Since thepositional information of partial images 1 and 2 is now known, adisplacement (distance A) between partial image 1 and image 2 can bedetermined. The travel time t is the time between two captured imageswhich can be obtained from the camera. Therefore, the travelling speedof the drawer is v=Alt.

The moving speed of the drawer may be used by the system to time theimaging of the drawer. From the speed measurement, the system mayanticipate when the next image should be taken. For instance if thespeed of the drawer is 5″/sec and cameras of the system have an imagecoverage of 2″, then the system can determine that it will need to takethe next image in ⅖ of a second to achieve no overlap of or gap betweenthe images. ⅕ of a second would provide a 1″ overlap between consecutiveimages. An acceleration of the travelling drawer can be determined bytaking the derivative of the drawer speed and track the acceleration ofthe drawer to further anticipate when to take the next image. Based onthe information of speed and acceleration, the system can conserveenergy for battery operation, and/or to speed up the performance of thesystem by properly timing the imaging of the drawer without capturingunnecessary images.

If the drawer gets twisted, and/or moves away from the camera, theperspective image will be rotated and distorted (skewed) as shown inFIG. 6 d. Such distortion may be detected based on a comparison of thecaptured images of the scales and the known positional relationship ofthe scales in the drawer. A correction of the distorted image may beobtained by adjusting or rotating the captured image to enable thecaptured scale image to correctly overlay on the correct position of thescales according to the stored reference data of the scales (see FIG. 6e). It is understood that any reference pattern with ampletwo-dimensional characteristics, such as a reference pattern withsufficient width, may be used to correct image skew or distortion in asimilar manner. Whenever a skew or distortion occurs, thetwo-dimensional characteristics of the reference pattern will differfrom characteristics of the reference data stored in the system. Acorrection of the distorted image may be obtained by adjusting orrotating the captured image to enable the captured pattern image to beconsistent to the stored reference data.

According to one embodiment, an exemplary system provides a pattern thatfills an region of interest of the camera to correct for distortions.The distortion required to change the image of the pattern to theexpected image of the pattern would then be applied to all futureimages. For example, as shown in FIG. 7 a, a known pattern, such as agrid of dots at ½″ spacing and being imaged essentially parallel to thecamera chip. Now all distortions in the optical system (lens, mirror . .. ) will be realized in the captured image of the grid. By distortingthe captured image to make the image of the grid pattern into the known,expected ½″ grid pattern, all the distortions of the system arecorrected for.

A detailed process for detection and compensating for imagingdistortions using known reference patterns is now described.

1. Determination the Centroids of Reference Patterns

A camera acquires a digital image of a flat plane containing a pluralityof identifiable image patterns at known locations on the plane. In thepresent embodiment, the camera's optical axis is assumed to be normal tothe plane, and each pixel of the digital image has a red, green and bluevalue, each between 0 and 255. These three values will be referred to asthe “color” or “RGB” value of a pixel. In the present embodiment, theknown patterns are circles of uniform color, and the surroundingbackground is of uniform color, and the color of the reference patternis different from that of the surrounding background.

In the present embodiment, a smoothed value is first computed for eachpixel in the digital image, and then a “color gradient” is computed foreach smoothed pixel in the digital image.

The smoothed RGB values for pixel at image column and row (x,y) arecomputed using the 3×3 smoothing kernel:

$\begin{matrix}1 & 2 & 1 \\2 & 4 & 2 \\1 & 2 & 1\end{matrix}$as follows:

smoothedPixel(x,y) = ( 1 * rawPixel(x−1,y−1) ) + ( 2 * rawPixel(x,y−1)) + ( 1 * rawPixel(x+1,y−1) ) + ( 2 * rawPixel(x−1,y ) ) + ( 4 *rawPixel(x,y ) ) + ( 2 * rawPixel(x+1,y ) ) + ( 1 * rawPixel(x−1,y+1)) + ( 2 * rawPixel(x,y+1) ) + ( 1 * rawPixel(x+1,y+1) )where “smoothedPixel” and “rawPixel” are the R, G or B values,respectively.

The color gradient is computed for each smoothed pixel in the digitalimage using the 3×3 Sobel filter kernels:

Kernel for X:

$\begin{matrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{matrix}$Kernel for Y:

$\begin{matrix}{- 1} & {- 2} & {- 1} \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{matrix}$as follows, where s is “smoothedPixel”:

gx = (−1 ⋆ s(x − 1, y − 1)) + 0 + (1 ⋆ s(x + 1, y − 1)) + (−2 ⋆ s(x − 1, y)) + 0 + (2 ⋆ s(x + 1, y)) + (−1 ⋆ s(x − 1, y + 1)) + 0 + (1 ⋆ s(x + 1, y + 1))gy = (−1 ⋆ s(x − 1, y − 1)) + (−2 ⋆ s(x, y − 1)) + (−1 ⋆ s + 1, y − 1)) + 0 + 0 + 0 + (1 ⋆ s(x − 1, y + 1)) + (2 ⋆ s(x, y + 1)) + (1 ⋆ s(x + 1, y + 1))gx and gy are computed for R, G and B, and the square of the colorgradient magnitude at pixel position x,y is then:gradSq(x,y)=Sum of R,G and B[(gx*gx)+(gy*gy)]

If the color of a smoothed pixel and its neighbors are close, this colorgradient will be small. Since the pattern color is different than thesurrounding background color, the color gradient of pixels at theboundary of pattern and background will be larger, and thus boundarypixels may be identified as those pixels having a color gradient largerthan a suitably defined threshold value. In the present embodiment, aregion of pixels enclosed by such a pixel boundary, where the parametersof the bounded region, such as size and shape, match the expectedparameters of a pattern, is identified as being potentially one of theknown patterns. The centroids of such regions are computed by dividingthe sum of the x or y values of the pixels in the bounded region by thenumber of pixels in the bounded region. If the distortion of the imagingsystem from an ideal “pinhole camera” system is not too large, thepattern of pattern centroid positions will be the same as that of theknown pattern on the plane, and the patterns thus measured may beindividually identified with individual known patterns.

2. Determining and Correcting Distortion

Let KXi and KYi be the known positions of pattern i, and let MXi and MYibe the measured centroid of the region identified as matching pattern i.Let:mxi=M*((cos(θ)*KXi)−(sin(θ)*KYi))+DXmyi=M*((cos(θ)*KYi)+(sin(θ)*KXi))+DY

M is a magnification factor, θ is a rotation angle, and DX and DY aretranslational offsets. M is the ratio of the known spacing of patternpositions in the plane and the measured spacing of corresponding patternpositions in the camera image, while 0, DX and DY are computed byleast-squares minimization of the sum of (mxi−MXi) squared plus(myi−MYi) squared, summed over all matched patterns, since this sum is afunction of variables 0, DX and DY, given the set of known values{KXi,KXi}, from which the set {mxi,myi} are computed, and thecorresponding set of measured values {Mxi,MYi}. mxi is thenapproximately equal to MXi, and myi is then approximately equal to Myi.

The distortion is then defined at the pixel position of pattern i as:distortionXi=MXi−mxidistortionYi=MYi−myi

The distortion at any pixel position in the “ideal” or undistorted imageis then computed by bilinear interpolation, using the distortions(distortionXi,distortionYi) at the four pattern centroid positions{mxi,myi}closest to that pixel position. In this manner, the systemcomputes and tabulates distortion correction values (distortionX,distortionY) at every undistorted image pixel position. Once thetabulated distortion correction values have been determined for aparticular camera, they may be applied to any future images acquired bythis camera, to produce an undistorted image. The color value for eachpixel in such an undistorted image is computed by taking the x,yposition of the pixel in the undistorted image, looking up thedistortion values (distortionX,distortionY) for the pixel at this x,yposition in the table, for this x,y in the undistorted image, andgetting the color value from the pixel at (x+distortionX, y+distortionY)in the (distorted) image captured by the camera. These corrections andapply to all future images. Points on the image to be corrected that donot lie on a point on the grid can be corrected by using an averagedistortion of the for closest grid points.

According to another embodiment, an exemplary tool storage system ofthis disclosure utilizes known patterns with predetermined positionalrelationships therebetween, or a known pattern that extends across atleast two cameras to measure a relative camera position (RCP) betweenthe cameras and uses this RCP to stitch future images by at least twocameras together.

A process for determining an RCP between two cameras is not described.Two cameras, whose physical and mechanical positions relative to eachother are fixed, each acquires a digital image of part of a flat planecontaining a plurality of identifiable image patterns at known locationson the plane. The images are corrected for distortion using thetechnique described in earlier regarding determining and correctingdistortion.” The centroids of these known patterns are then determinedby the technique described in the section determining the centroids ofpatterns. A point on the plane visible in both camera's fields of view,such as the centroid of a particular pattern, will project to position(x1,y1) in the corrected (undistorted) image of the first camera, andwill project to position (x2,y2) in the corrected (undistorted) image ofthe second camera. It is assumed that the optical axes of the twocameras are parallel, and that their magnifications are the same.

Then (x1,y1) and (x2,y2) are related by the following transform:x2=(cos(θ)*x1)−(sin(θ)*y1)+dxy2′=(cos(θ)*y1)+(sin(θ)*x1)+dywhere θ is a rotation angle, and dx and dy are translational offsets,which are computed by least-squares minimization of the sum of (x2 i′−x2i) squared plus (y2 i′−y2 i) squared, summed over all patterns i visiblein both camera's fields of view, since this sum is a function ofvariables 0, dx and dy, given the set of values {x1 i,y1 i}, from whichthe set {x2 i′,y2 i′} are computed, and the corresponding set of values{x2 i,y2 i}. x2 i′ is then approximately equal to x2 i, and y2 i′ isthen approximately equal to y2 i. The parameters 0, dx and dy thendefine the RCP transform relating the pair of cameras.

The inverse transform is:x1=(cos(θI)*x2)−(sin(θI)*y2)+dxIy1=(cos(θI)*y2)+(sin(θI)*x2)+dyIwhere:dxI=−((cos(θI)*dx)−(sin(θI)*dy)dyI=−((cos(θI)*dy)+(sin(θI)*dx)

Thus, for any point on the plane visible in both camera's fields of viewwhich projects to position (x1,y1) in the corrected (undistorted) imageof the first camera, and projects to position (x2,y2) in the corrected(undistorted) image of the second camera, (x1,y1) and (x2,y2) arerelated by this transform.

Also, within a scale factor relating dimensions on the plane to pixelsin the camera images, any point on the plane (xp,yp) may be related toeither (x1,y1) or (x2,y2) by a transform of the same mathematical form.

Likewise, given a transform of the same mathematical form relatingpoints in a first plane (x1,y1) to points in a second plane (x2,y2),with parameters {θ12,dx12,dy12}, and a transform of the samemathematical form relating points in the second plane (x2,y2) to pointsin a third plane (x3,y3), with parameters {θ23,dx23,dy23}, the transformof the same mathematical form relating points in a first plane (x1,y1)to points in the third plane (x3,y3), has parameters {θ13,dx13,dy13}given by:θ13=θ12+θ23dx13=(cos(θ23)*dx12)−(sin(θ23)*dy12)+dx23dy13=(cos(θ23)*dy12)+(sin(θ23)*dx12)+dy23

As illustrated in FIG. 7 b, a target or an array of targets that spansacross the field of view of all the cameras is provided. Part of thetarget or a target of an array of targets can be seen by each camera.Knowing the targets position with respect to each other, the camerasposition with respect to each other can be measured (FIG. 7 c). Use thelocation of the relative cameras to create offsets in X and Y and arotation of each camera to stitch one single image together (see FIG. 7d).

Imaging Setting Control

If the image sensing devices of the imaging subsystem requireillumination to obtain acceptable image quality, illumination devicesmay be provided. For example, LEDs may be used to illuminate an area tobe imaged by the image sensing devices. It is understood that numeroustypes of illumination sources may be used. For example, LEDs may bedisposed surrounding lens or image sensors of the image sensing devices.Light emitted by the LEDs is transmitted along the same path as thecamera view. In another embodiment, a light directing device, such as amirror, may be used to direct lights emitted by an illumination sourcetowards a target area, such as a drawer to be imaged. The timing andintensity of the illumination is controlled by the same processor thatcontrols the camera and its exposure. In some possible configurations ofcameras it may be desirable to implement background subtraction toenhance the image. Background subtraction is a well known imageprocessing technique use to remove undesirable static elements from animage. First an image is captured with illumination off. Then a secondimage is captured with illumination on. The final image is created bysubtracting the illumination off image from the illumination on image.Image elements that are not significantly enhanced by the illuminationare thereby removed from the resulting image.

In order to properly image drawers of varying distances to a camera,appropriate settings need to be used in capturing images and/orprocessing the images after they are captured. These settings includeillumination intensities, camera exposure time, color gains includingindividual parameters for each color filter in the image sensor array,aperture configurations, aperture sizes, shutter speed, f stop,threshold values for post-capturing image processing, etc. Furthermore,imaging parameters other than illuminations and exposures may differfrom drawer to drawer and should be addressed during the imagingprocess. These additional parameters or considerations include, forexample, location and size of a region of interest (ROI) for imagingwithin camera view; whether an ROI is redundantly imaged such thatcertain cameras could be shut down or turned off for power savings; andeffects of parallax on ROI.

Conventional imaging techniques usually determine those needed settingsbased on real time detections of a distance from the camera to a targetobject, and then calculate needed parameters and settings on the fly.However, such real time calculations require fast and powerfulprocessors which adds cost to the system. This disclosure provides aunique algorithm that substantially reduces the calculation load to thesystem and significantly speed up the calculation process.

An exemplary tool storage system according to this disclosure pre-storesreference data including a distance from each respective drawer to eachcamera and needed parameter settings according to the identity of eachrespective drawer and different lighting conditions in the environment.This reference data may be generated during the manufacture of the toolstorage system. A distance from each respective camera to eachrespective drawer is measured and recorded. Ideal imaging parametersunder different illumination conditions are determined for each recordeddistance through standard means and stored in a look-up table. Thelook-up table also stores additional information corresponding to eachrespective drawer, including locations and sizes of region of interest(ROI) for imaging within camera view, whether an ROI is redundantlyimaged such that certain cameras could be shut down or turned off forpower savings; and effects of parallax on ROI.

During operation, the exemplary tool storage system determines theidentity of a subject drawer being accessed and lighting conditions inthe environment. According to the determined identity of drawer and thedetected lighting conditions, the exemplary system accesses thereference data including pre-stored distance information from eachcamera corresponding to the subject drawer and ideal imaging parametersaccording to the detected illumination conditions and the distance. Suchapproach removes the need for the system processor to perform complexdetections and calculations to determine a distance to a subject drawerand appropriate imaging parameters for imaging the drawer. Rather, allthe needed information is accessible from the pre-stored look-up table.Accordingly, suitable parameters and/or settings may be used and/oradjusted to capture images or processing of a respective drawer, so thatimages with sufficient quality may be obtained. In a system withmultiple cameras, a single camera can be used to set the gross parameterlevels for all cameras and illumination controls.

The exemplary system may determine the identity of a drawer beingaccessed using various manners. According to one embodiment, each draweris associated with an identifier viewable to a camera when the drawerenters the field of view of the camera. The camera determines theidentity of the drawer according to the unique identifier. For instance,the identifier may be a pattern unique to each drawer, such as a barcode, an identification number, a unique pattern, etc. The identifiermay use the same pattern for all drawers but its location is unique toeach drawer. It is also possible to use the same pattern placed at aconstant location away from the center axis of the camera and parallaxmay be used to identify targets.

During operation, an initial image of a drawer is processed to searchfor the unique pattern that will allow the system to determine a targetis in view and determine the actual distance to the target. This allowsthe system to determine which previously calculated illumination andexposure settings to use for subsequent images. The identifying patternmay be a separate unique image pattern for each target distance, or itmay be a common pattern whose size or location allows determination ofthe target distance. The identifying pattern is designed to present ahigh contrast image to the camera that can be decoded under a wide rangeof illumination and exposure controls. For instance, a pattern of blackcircles on a white background may be used. According to still anotherembodiment, each drawer is provided with two reference patterns withknown positional relationship, such as a known distance. Since thedrawers are progressively further away from the camera, and the image ofthe drawer and the distance between reference patterns also get smaller.A look-up table of different distances between the reference patternsand the corresponding identities of drawer is stored in the system. Whenan image of a drawer is captured, the system uses the distance betweenthe reference patterns (in pixels) and the look-up table to determinewhich drawer is being opened.

In another embodiment, each drawer is associated with a movement sensor,a contact sensor or a position detector for detecting a movement of eachdrawer. The exemplary system determines the identity of the drawer thatrequires imaging by determining which sensor or detector is beingactivated by a moving drawer, or based on signals transmitted by theactivated sensor or detector.

The exemplary tool storage system uses a unique approach to performcolor calibration and/or adjust color gains. Each drawer is providedwith a “color-coded” area with preset color attributes. The color-codedarea may be any area such as background in known color, foam in knowncolor, a sticker with known color, etc. Information related to thepreset color attributes is stored in a non-volatile memory in thesystem.

The color-coded area is imaged under an initial condition. The image isacquired from camera sensors in a format referred to as “raw” or “rawbayer.” The sensor data in raw format can be converted to various colorspaces using mathematical transforms well-known to people skilled in theart, such as IHS (intensity, hue, and saturation), to allow forextracting attributes of image taken under the initial condition. Theresults of the conversion may be improved by using any number ofstatistical averaging approaches. A color offset value is generatedbased on a difference between the color attributes of the capturedcolor-coded area and the preset color attributes. According to theoffset value, the system determines whether any settings of the camera,such as color gains, exposures, etc., should be adjusted to achieve anappropriate image quality. In one embodiment, such adjustments areapplied to all subsequent images. According to another embodiment, thesystem performs color calibration as discussed above for each respectiveimage.

According to another embodiment, the initial image of the color-codedarea is stored as baseline image information. All subsequent images ofthe color-coded area are compared with the baseline image to determinean offset. The system then determines, according to the calculatedoffset, whether any settings of the camera, such as color gains,exposures, etc., should be adjusted to match the baseline information.For example, for each attribute, a difference between the baselineinformation and subsequent images may be calculated and compared with apreset threshold value. If the difference exceeds the threshold value,the system proceeds performing an adjustment on the subsequent images.Otherwise, no adjustment functions will be performed on the images. Asdiscussed earlier, the adjustments could be made when the images aretaken or applied to the capture images after they are taken by the imagesensing devices.

Techniques for adjusting attributes of images are well-known and willnot be repeated. For instance, hue can be adjusted by varying the gainratio between colors. If a white area looks like light blue, the bluedetector gain can be reduced to make the area look white. For adjustingcolor gains and exposures, a camera has separate gain adjustments forfour bayer pattern filter settings. A ratio of gains may be adjustedbetween various filter elements at the camera level. The exposure can beadjusted by changing the exposure time of the image during acquisitionor imaging, applying an image gain as a post processing step, or byincreasing light output from the illumination hardware. The intensity ofthe color is related to the total light returned by all color filters.If a given intensity is lower than the ideal value, all the gains can beincreased, the camera exposure time can be increased, or theillumination can be set to a higher level. Saturation relates to thebrilliance of a given color. Again, exposure and gain can be adjusted tomatch ideal levels with actual levels.

Networked Storage Systems

Storage systems described in this disclosure may be linked to a remoteserver in an audit center, such that inventory conditions in eachstorage system is timely updated and reported to the server. As shown inFIG. 9, a server 802 is coupled to multiple storage systems 800 via awireless network. Server 802 may include a database server, such as aMicrosoft SQL server. Information related to authentication, authorizedusers, inventory conditions, audit trails, etc., is stored in thedatabase.

In one embodiment, each storage system 800 is provided with a datatransceiver, such as an 802.11g or Ethernet module. The Ethernet moduleconnects directly to the network, while a 802.11g module may connectthrough a 802.11g router connected to the network. Each of these networkmodules will be assigned a static or dynamic IP address. In oneembodiment, storage systems 800 check in to the server through the datatransceivers on a periodic basis, to download information aboutauthorized users, authorization levels of different users or differentkeycards, related storage systems, etc. Storage systems 800 also uploadinformation related to the systems such as inventory conditions, drawerimages, tool usage, access records, information of user accessingstorage systems 800, etc., to server 802. Each storage system 800 may bepowered by an AC source or by a battery pack. An uninterruptible powersupply (UPS) system may be provided to supply power during a powerfailure.

Server 802 allows a manager or auditor to review access informationrelated to each storage system 800, such as inventory conditions andinformation related to each access to storage system 800 like userinformation, usage duration, inventory conditions, changes in inventoryconditions, images of drawers or contents of the storage system, etc. Inone embodiment, sear 802 may form a real time connection with a storagesystem 800 and download information from that storage system. Themanager or auditor may also program the access control device on eachstorage system through server 802, such as changing password, authorizedpersonnel, adding or deleting authorized users for each storage system,etc. Authorization data needed for granting access to each storagesystem 800 may be programmed and updated by server 802 and downloaded toeach storage system 800. Authorization data may include passwords,authorized personnel, adding or deleting authorized users for eachstorage system, user validation or authentication algorithm, public keyfor encryptions and/or decryptions, black list of users, white list ofusers, etc. Other data updates may be transmitted to each storage systemfrom server 802, such as software updates, etc. Similarly, any changesperformed on storage system 800, such as changing password, adding ordeleting authorized users, etc., will be updated to server 802.

For each access request submitted by a user, a storage systemauthenticates or validates the user by determining a user authorizationaccording to user information input by the user via the data inputdevice and the authorization data. According to a result of theauthentication, the data processor selectively grants access to thestorage system by controlling an access control device, such as a lock,to grant access to the storage system 800 or one or more storage drawersof one or more storage systems 800.

Server 802 also allows a manager to program multiple storage systems 800within a designated group 850 at the same time. The manager may selectwhich specific storage systems should be in group 850. Once a user isauthorized access to group 850, the user has access to all storagesystems within group 850. For instance, a group of storage systemsstoring tools for performing automotive tools may be designated as anautomotive tool group, while another group of storage systems storingtools for performing electrical work may be designated as an electricaltool group. Any settings, adjustments or programming made by Server 802in connection with a group automatically apply to all tool storagesystems in that group. For instance, server 802 may program the toolstorage systems by allowing an automotive technician to access all toolstorage systems in the automotive tool group, but not those in theelectrical tool group. In one embodiment, each system 800 only includesminimal intelligence sufficient for operation. All other dataprocessing, user authentication, image processing, etc., are performedby server 802.

Similarly, server 802 also allows a manager to program multiple storagedrawers 330 within a designated group at the same time. The manager mayselect which specific storage drawers, of the same system or differentstorage systems, should be in the group. Once a user is authorizedaccess to the group, the user has access to all storage drawers withinthe group. For instance, a group of storage systems storing tools forperforming automotive tools may be designated as an automotive toolgroup, while another group of storage systems storing tools forperforming electrical work may be designated as an electrical toolgroup.

In another embodiment, an exemplary networked storage system as shown inFIG. 9 utilizes hierarchical authorization architecture to manage accessto storage systems. One or more storage systems 800 are given the statusof master storage system. Each master storage system has one or moreassociated slave storage systems. If a user is authorized to access to amaster storage system, the same user is automatically authorized toaccess any slave storage system associated with that master system. Onthe other hand, if a user is authorized to access a slave storagesystem, the authorization to the slave system does not automaticallygrant the user access to its associated master storage system or otherslave storage systems associated with the same master storage system.

According to still another embodiment, an exemplary networked storagesystem as shown in FIG. 9 grants user access by utilizing multiplehierarchical authorization levels. Each authorization level isassociated with pre-specified storage systems, which can be programmedby a manager via server 802. When a user is assigned a specificauthorization level, this user is authorized to access all storagesystems associated with the assigned authorization level and all storagesystems associated with all authorization levels lower than the assignedauthorization level in the authorization hierarchy, but not to thoseassociated with authorization levels higher than the assignedauthorization level in the authorization hierarchy.

Audit

An exemplary inventory control system according to this disclosuretracks various types of information related to each access. For example,system 800 records date, time and/or duration for each access, andcorresponding user information submitted by a user to obtain access tosystem 800. As discussed earlier, system 800 captures one or more imagesof the storage unit during each access for determining an inventorycondition. The images are linked to each access and accessing user andstored in system 800. System 800 may store the information locally orupload the obtained information to server 802 via the wirelesscommunication network, as shown in FIG. 9.

Server 802 may process and compile the information received from eachsystem 800, to create an audit trail for each server 802. The audittrail is accessible by managers or users with suitable authorizationlevels. Different types of audit trails may be generated and retrievedbased on preference of authorized users. For instance, an audit trailmay be generated for one or more specific dates, one or more specificusers, one or more specific tools, one or more IDs, etc. Additionalinformation and analysis may be generated and provided by server 802.For example, system 802 may track usages of a specific tool over time,and generate a report summarizing a usage frequency for each tool forevaluation. Such report may be used to determine what tools are usedmore frequently and which tools probably are not needed because they areused less often than others. The data can be sorted by pertinentcriteria such as date, user, or drawer, allowing the auditor to see theexact state of the drawer when it was opened and when it was closed.

If a particular tool is not properly detected, due to interference bysome foreign objects or for whatever reason, audit center 802 generatesa warning with an image of the specific area of the drawer that has anissue. This is accomplished by requesting the image for the affectedarea from the imaging subsystem.

According to one embodiment, system 800 displays the status of the toolstorage system through a screen saver. A display of system 800 has acustom screen saver that displays in one of three different colorsdepending on the status of the tool control system. If all tools arechecked in and the box is closed, the screen saver displays a greenbouncing icon. If tools are currently checked out but the box is closed,a yellow bouncing icon is displayed. If the system is currently open ared icon is displayed.

According to another embodiment, the display of system 800 is providedwith a graphical presentation of the tool storage system thatdynamically updates the status of tool storage system 800. An image oftool storage system 800 is shown and the drawers will reflect thecurrent status of the system. If a drawer is open, that drawer will bedisplayed in red on the display. If a drawer is closed but tools arecurrently checked out, that drawer will appear yellow. If the drawer isclosed and all tools are present in the drawer, the drawer will be clearwith a black outline.

FIG. 10 a shows an exemplary screen of an audit trail with respect to aspecific storage system 800. Each access to system 800 is identified byDate/Time 920 and user information 910 of users associated with eachaccess. User information may include any information submitted by a userwhen requesting access to system 800, such as finger prints, facialrecognition images, user images taken by user cameras, passwords,information stored in keycards, any information for authentication, etc.In one embodiment, data of user facial characteristics of each user isstored in system 800 or server 802. For each access, an image of a useraccessing system 800 is captured by a user camera. User informationsubmitted by the user for gaining access to system 800, such asinformation stored in a keycard and/or password, is collected. Thecaptured image is compared against user facial characteristics of a useridentified by the user information. System 800 or server 802 determineswhether the facial characteristics of the user accessing system 800matches facial characteristics of the user identified by the userinformation.

One or more images are taken during each access to storage system 800.FIG. 10 b shows an exemplary “before access” image taken by cameras ofsystem 800 before a user has access to the storage locations or when thedrawer is moving in the first direction, as discussed earlier in thisdisclosure. As shown in FIG. 10 b, each tool is properly stored in itscorresponding storage location. FIG. 10 c shows an exemplary “afteraccess” image taken by cameras of system 800 after the access isterminated or when a storage drawer moves in the second direction asdiscussed earlier. As shown in FIG. 10 c, tools corresponding to storagelocations 951 and 952 are missing. Based on the image shown in FIG. 10c, system 800 determines that tools in storage locations 951 and 952 aremissing. An audit trail is generated regarding the missing tools and theuser associated with the access. FIG. 10 d shows an exemplary recordstored in system 800 and/or server 802, in which both “before access”and “after access” images 981 and 982 are stored. Missing tools areidentified according to “after access” image 982 and listed in area 980.

In the previous descriptions, numerous specific details are set forth,such as specific materials, structures, processes, etc., in order toprovide a thorough understanding of the present disclosure. However, asone having ordinary skill in the art would recognize, the presentdisclosure can be practiced without resorting to the detailsspecifically set forth. In other instances, well known processingstructures have not been described in detail in order not tounnecessarily obscure the present disclosure.

Only the illustrative embodiments of the disclosure and examples oftheir versatility are shown and described in the present disclosure. Itis to be understood that the disclosure is capable of use in variousother combinations and environments and is capable of changes ormodifications within the scope of the inventive concept as expressedherein.

What is claimed is:
 1. An inventory control system for determining aninventory condition of objects stored in the system, the systemcomprising: at least one storage drawer, each storage drawer including aplurality of storage locations for storing objects; a data storagedevice storing reference data for each storage drawer, wherein thereference data includes, for each region of interest corresponding toeach respective storage drawer, a reference image signature representinga pixel number distribution of the region of interest when a storedobject is present, wherein the pixel number distribution includes aplurality of pixel numbers; and a data processor configured to: derivean image signature representing a pixel number distribution of a regionof interest corresponding to a drawer using a captured image of thedrawer; and determine an inventory condition of the drawer according tothe stored reference data corresponding to the region of interest andthe derived image signature representing the pixel number distributionof the region of interest in the captured image by matching each of theplurality of pixel numbers of the pixel number distribution in thestored reference image signature with the pixel number distribution ofthe region of interest in the captured image of the drawer.
 2. Thesystem of claim 1, wherein the pixel number distribution of thereference data includes a histogram.
 3. The system of claim 2, whereinthe data processor generates the image signature of the image by:accessing data including the pixel number distribution relative topreset image attributes; identifying a group of pixels sharing a set ofcommon image attributes based on a number of pixels sharing the commonimage attributes; identifying one or more groups of pixels sharing adifferent set of common image attributes based on a number of pixelssharing the different set of common image attributes; generating theimage signature based on the common image attributes of the identifiedpixel groups.
 4. The system of claim 1, wherein: each storage locationis configured to store a pre-designated tool; each storage location isassociated with one or more pre-defined regions of interest; and thedata processor determines an inventory condition of the pre-designatedtool of a respective storage location based on a correlation of theimage signature of each region of interest associated with the storagelocation, and the reference image signature corresponding to each regionof interest associated with the storage location.
 5. A method for usingin an inventory control system for determining an inventory condition ofobjects stored in the system, the system comprising at least one storagedrawer for storing objects, a data storage device storing reference datafor each storage drawer, wherein the reference data includes, for eachregion of interest corresponding to each respective storage drawer, areference image signature representing a pixel number distribution ofthe region of interest when a stored object is present wherein the pixelnumber distribution includes a plurality of pixel numbers, the methodcomprising: capturing an image of a drawer; deriving an image signaturerepresenting a pixel number distribution of at least one region ofinterest corresponding to the drawer; and determining an inventorycondition of the drawer according to the stored reference datacorresponding to the region of interest corresponding to the drawer andthe derived image signature representing the pixel number distributionof the region of interest in the captured image by matching each of theplurality of pixel numbers of the pixel number distribution in thestored reference image signature with the pixel number distribution ofthe region of interest in the captured image of the storage drawer. 6.The method of claim 5, wherein the pixel number distribution of thereference data includes a histogram.
 7. The method of claim 6, whereinthe image signature of the image is derived by: accessing data includingthe pixel number distribution relative to preset image attributes;identifying a group of pixels sharing a set of common image attributesbased on a number of pixels sharing the common image attributes;identifying one or more groups of pixels sharing a different set ofcommon image attributes based on a number of pixels sharing thedifferent set of common image attributes; generating the image signaturebased on the common image attributes of the identified pixel groups. 8.The method of claim 5, wherein: each storage location is configured tostore a pre-designated tool; each storage location is associated withone or more pre-defined regions of interest; and the data processordetermines an inventory condition of the pre-designated tool of arespective storage location based on a correlation of the imagesignature of each region of interest associated with the storagelocation, and the reference image signature corresponding to each regionof interest associated with the storage location.
 9. An inventorycontrol system for determining an inventory condition of objects storedin the system, the system comprising: at least one storage drawer, eachstorage drawer including a foam layer forming a plurality of storagelocations for storing objects, wherein each storage location isconfigured to store a pre-designated object and each storage location isformed by a cutout of the foam layer corresponding an object stored inthe storage location; an image sensing device configured to capture animage of one of the storage drawers; a data storage system storingreference data corresponding to each storage drawer, wherein thereference data includes information of one or more pre-defined regionsof interest corresponding to each storage location in the drawer, andobjects stored in each storage location in the drawer; and a dataprocessor configured to: access the captured image of the drawer; accessthe reference data corresponding to the drawer; determine an inventorycondition of the drawer based on the stored reference data correspondingto the drawer and the captured image of the storage drawer; wherein thereference data is adapted from a data file based on which cutouts of thefoam layer are created; wherein the reference data includes, for eachregion of interest corresponding to each respective storage drawer, areference image signature representing a pixel number distribution ofthe region of interest when a stored object is present, wherein thepixel number distribution includes a plurality of pixel numbers; andwherein the data processor determines the inventory condition of thedrawer by matching each of the plurality of pixel numbers of the pixelnumber distribution in the stored reference image signature with a pixelnumber distribution of a region of interest in the captured image of thedrawer.
 10. The system of claim 9, wherein the data processor importsthe data file based on which cutouts of the foam layer are created andderive the reference data from the data file.
 11. A method for preparingreference data for use in an inventory control system for determining aninventory condition of objects stored in the system, the systemincluding at least one storage drawer, each storage drawer including afoam layer forming a plurality of storage locations for storing objects,wherein each storage location is configured to store a pre-designatedobject and each storage location is formed by a cutout of the foam layercorresponding to an object stored in the storage location, the systemdetermining the inventory condition of a respective drawer based on acaptured image of the respective drawer and the reference data, themethod comprising: importing, from a database, a data file based onwhich cutouts of the foam layer for a respective drawer is created,wherein the data files includes positional information of each cutout inthe respective drawer; importing, from the database, a data file basedon, for each cutout corresponding to each respective storage drawer, areference image signature representing a pixel number distribution ofthe cutout when a stored object is present, wherein the pixel numberdistribution includes a plurality of pixel numbers; and using a dataprocessing device to create a data structure specifying positionalinformation of each cutout in each respective drawer and at least oneregion of interest corresponding to each cutout, according to theimported data files, wherein the pixel number distribution stored in thedata structure created by the data processing device is sufficient todetermine inventory conditions of objects stored in the system from thecaptured image of the respective drawer.
 12. The system of claim 1,wherein the reference data further includes a reference image signaturerepresenting a pixel number distribution of the region of interest whenthe stored object is not present, and the data processor determines thatthe object associated with the region of interest is missing from thedrawer upon matching the derived image signature to the stored referenceimage signature representing the pixel number distribution of the regionof interest when the stored object is not present.
 13. The method ofclaim 5, wherein the reference data further includes a reference imagesignature representing a pixel number distribution of the region ofinterest when the stored object is not present, and the determining theinventory condition of the drawer includes determining that the objectassociated with the region of interest is missing from the drawer uponmatching the derived image signature to the stored reference imagesignature representing the pixel number distribution of the region ofinterest when the stored object is not present.
 14. The system of claim9, wherein the reference data further includes a reference imagesignature representing a pixel number distribution of the region ofinterest when the stored object is not present, and the data processordetermines that the object associated with the region of interest ismissing from the drawer upon matching the derived image signature to thestored reference image signature representing the pixel numberdistribution of the region of interest when the stored object is notpresent.
 15. The system of claim 1, wherein the data processordetermines that the object associated with the region of interest ispresent in the drawer when the pixel number distribution of the derivedimage signature matches the pixel number distribution of the storedreference image signature within a preset percentage tolerance.
 16. Themethod of claim 5, wherein the object associated with the region ofimage signature matches the pixel number distribution of the storedreference image signature within a preset percentage tolerance.
 17. Thesystem of claim 9, wherein the data processor determines that the objectassociated with the region of interest is present in the drawer when thepixel number distribution of the derived image signature matches thepixel number distribution of the stored reference image signature withina preset percentage tolerance.